# TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models

arXiv: https://arxiv.org/abs/2408.00735 · HTML: https://mlsanity.com/paper/2408.00735/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 48 of its 67 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 48)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion](https://mlsanity.com/paper/2310.02279/) (2023)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [Image2StyleGAN++: How to Edit the Embedded Images?](https://mlsanity.com/paper/1911.11544/) (2019)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [SDXL-Lightning: Progressive Adversarial Diffusion Distillation](https://mlsanity.com/paper/2402.13929/) (2024)
- [ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement](https://mlsanity.com/paper/2104.02699/) (2021)

## Built on by (most cited first, 30 of 64)

- [FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models](https://mlsanity.com/paper/2412.08629/) (2024)
- [Stable Flow: Vital Layers for Training-Free Image Editing](https://mlsanity.com/paper/2411.14430/) (2024)
- [FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing](https://mlsanity.com/paper/2412.07517/) (2024)
- [Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models](https://mlsanity.com/paper/2411.07232/) (2024)
- [SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion](https://mlsanity.com/paper/2412.04301/) (2024)
- [SliderSpace: Decomposing the Visual Capabilities of Diffusion Models](https://mlsanity.com/paper/2502.01639/) (2025)
- [SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation](https://mlsanity.com/paper/2412.01801/) (2024)
- [ChatGarment: Garment Estimation, Generation and Editing via Large Language Models](https://mlsanity.com/paper/2412.17811/) (2024)
- [ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions](https://mlsanity.com/paper/2506.03107/) (2025)
- [ChordEdit: One-Step Low-Energy Transport for Image Editing](https://mlsanity.com/paper/2602.19083/) (2026)
- [IntrinsicEdit: Precise generative image manipulation in intrinsic space](https://mlsanity.com/paper/2505.08889/) (2025)
- [REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations](https://mlsanity.com/paper/2502.03629/) (2025)
- [InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention](https://mlsanity.com/paper/2412.06753/) (2024)
- [Learning an Image Editing Model without Image Editing Pairs](https://mlsanity.com/paper/2510.14978/) (2025)
- [Instruction-based Image Manipulation by Watching How Things Move](https://mlsanity.com/paper/2412.12087/) (2024)
- [WeatherEdit: Controllable Weather Editing with 4D Gaussian Field](https://mlsanity.com/paper/2505.20471/) (2025)
- [PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing](https://mlsanity.com/paper/2410.04844/) (2024)
- [Adams Bashforth Moulton Solver for Inversion and Editing in Rectified Flow](https://mlsanity.com/paper/2503.16522/) (2025)
- [SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder](https://mlsanity.com/paper/2510.05081/) (2025)
- [FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields](https://mlsanity.com/paper/2507.08285/) (2025)
- [PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models](https://mlsanity.com/paper/2502.04050/) (2025)
- [Tight Inversion: Image-Conditioned Inversion for Real Image Editing](https://mlsanity.com/paper/2502.20376/) (2025)
- [In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulation](https://mlsanity.com/paper/2505.20271/) (2025)
- [InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow](https://mlsanity.com/paper/2508.06033/) (2025)
- [Describe, Don't Dictate: Semantic Image Editing with Natural Language Intent](https://mlsanity.com/paper/2508.20505/) (2025)
- [Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization](https://mlsanity.com/paper/2511.05616/) (2025)
- [Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation](https://mlsanity.com/paper/2412.02631/) (2024)
- [Cora: Correspondence-aware image editing using few step diffusion](https://mlsanity.com/paper/2505.23907/) (2025)
- [OmniRefiner: Reinforcement-Guided Local Diffusion Refinement](https://mlsanity.com/paper/2511.19990/) (2025)
- [Spanning the Visual Analogy Space with a Weight Basis of LoRAs](https://mlsanity.com/paper/2602.15727/) (2026)

## Abstract (arXiv)

Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards process, and adapting them to distilled, fast-sampling methods has proven surprisingly challenging. Here, we focus on a popular line of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We trace the artifacts to mismatched noise statistics between inverted noises and the expected noise schedule, and suggest a shifted noise schedule which corrects for this offset. To increase editing strength, we propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts. All in all, our method enables text-based image editing with as few as three diffusion steps, while providing novel insights into the mechanisms behind popular text-based editing approaches.